






'''simple docstring'''




from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer

@dataclass
class   _UpperCAmelCase     (	UpperCAmelCase__		):




   """simple docstring"""

   snake_case       							=		42





class   _UpperCAmelCase     (	UpperCAmelCase__				,       UpperCAmelCase__		):




   """simple docstring"""

   @register_to_config
   def __init__(						self					:      List[str] , __UpperCAmelCase					:      int = 3 , __UpperCAmelCase					:      int = 3 , __UpperCAmelCase					:      Tuple[str] = ("DownEncoderBlock2D",) , __UpperCAmelCase					:      Tuple[str] = ("UpDecoderBlock2D",) , __UpperCAmelCase					:      Tuple[int] = (64,) , __UpperCAmelCase					:      int = 1 , __UpperCAmelCase					:      str = "silu" , __UpperCAmelCase					:      int = 3 , __UpperCAmelCase					:      int = 32 , __UpperCAmelCase					:      int = 256 , __UpperCAmelCase					:      int = 32 , __UpperCAmelCase					:      Optional[int] = None , __UpperCAmelCase					:      float = 0.18215 , __UpperCAmelCase					:      str = "group" , ):





         '''simple docstring'''





         super().__init__()

         # pass init params to Encoder
         _A     	=      Encoder(
             in_channels=__UpperCAmelCase , out_channels=__UpperCAmelCase , down_block_types=__UpperCAmelCase , block_out_channels=__UpperCAmelCase , layers_per_block=__UpperCAmelCase , act_fn=__UpperCAmelCase , norm_num_groups=__UpperCAmelCase , double_z=__UpperCAmelCase , )

         _A     	=      vq_embed_dim if vq_embed_dim is not None else latent_channels

         _A     	=      nn.Convad(__UpperCAmelCase , __UpperCAmelCase , 1						)
         _A     	=      VectorQuantizer(__UpperCAmelCase , __UpperCAmelCase , beta=0.25 , remap=__UpperCAmelCase , sane_index_shape=__UpperCAmelCase						)
         _A     	=      nn.Convad(__UpperCAmelCase , __UpperCAmelCase , 1						)

         # pass init params to Decoder
         _A     	=      Decoder(
             in_channels=__UpperCAmelCase , out_channels=__UpperCAmelCase , up_block_types=__UpperCAmelCase , block_out_channels=__UpperCAmelCase , layers_per_block=__UpperCAmelCase , act_fn=__UpperCAmelCase , norm_num_groups=__UpperCAmelCase , norm_type=__UpperCAmelCase , )

   @apply_forward_hook
   def 		lowerCAmelCase					(						self					:      Dict , __UpperCAmelCase					:      torch.FloatTensor , __UpperCAmelCase					:      bool = True						):





         '''simple docstring'''





         _A     	=      self.encoder(__UpperCAmelCase						)
         _A     	=      self.quant_conv(__UpperCAmelCase						)

         if not return_dict:
               return (h,)

         return VQEncoderOutput(latents=__UpperCAmelCase						)

   @apply_forward_hook
   def 		lowerCAmelCase					(						self					:      Any , __UpperCAmelCase					:      torch.FloatTensor , __UpperCAmelCase					:      bool = False , __UpperCAmelCase					:      bool = True						):





         '''simple docstring'''





         if not force_not_quantize:
               _A	,      _A	,      _A     	=      self.quantize(__UpperCAmelCase						)
         else:
               _A     	=      h
         _A     	=      self.post_quant_conv(__UpperCAmelCase						)
         _A     	=      self.decoder(__UpperCAmelCase , quant if self.config.norm_type == "spatial" else None						)

         if not return_dict:
               return (dec,)

         return DecoderOutput(sample=__UpperCAmelCase						)

   def 		lowerCAmelCase					(						self					:      Tuple , __UpperCAmelCase					:      torch.FloatTensor , __UpperCAmelCase					:      bool = True						):





         '''simple docstring'''





         _A     	=      sample
         _A     	=      self.encode(__UpperCAmelCase						).latents
         _A     	=      self.decode(__UpperCAmelCase						).sample

         if not return_dict:
               return (dec,)

         return DecoderOutput(sample=__UpperCAmelCase						)






'''simple docstring'''




def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       int:

    def count_of_possible_combinations(UpperCamelCase__							) -> int:
        if target < 0:
            return 0
        if target == 0:
            return 1
        return sum(count_of_possible_combinations(target - item							) for item in array							)

    return count_of_possible_combinations(UpperCamelCase__							)






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       int:

    def count_of_possible_combinations_with_dp_array(
        UpperCamelCase__							,    UpperCamelCase__							) -> int:
        if target < 0:
            return 0
        if target == 0:
            return 1
        if dp_array[target] != -1:
            return dp_array[target]
        __lowerCamelCase            =    sum(
            count_of_possible_combinations_with_dp_array(target - item							,    UpperCamelCase__							)
            for item in array							)
        __lowerCamelCase            =    answer
        return answer

    __lowerCamelCase            =    [-1] * (target + 1)
    return count_of_possible_combinations_with_dp_array(UpperCamelCase__							,    UpperCamelCase__							)






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       int:
    __lowerCamelCase            =    [0] * (target + 1)
    __lowerCamelCase            =    1

    for i in range(1							,    target + 1							):
        for j in range(UpperCamelCase__							):
            if i - array[j] >= 0:
                dp_array[i] += dp_array[i - array[j]]

    return dp_array[target]


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  __UpperCAmelCase					=3
  __UpperCAmelCase					=5
  __UpperCAmelCase					=[1, 2, 5]
  print(combination_sum_iv(n, array, target))
